The NIH has declared its intention to leverage the power of science to identify health interventions that yield the greatest benefits by assessing their relative effectiveness and cost-effectiveness in real world settings. Thus, evidence-based approaches will guide the translation of basic biomedical research into routine clinical practice and public health policy. The NIH?s commitment aligns with the Sydney Declaration, signed by more than 2,000 public health professionals worldwide. This Declaration recommends that 10% of all resources dedicated to HIV programming be used for research towards optimizing interventions utilized and health outcomes achieved, and asserts that without such a sustained effort, ending the HIV/AIDS epidemic will not be possible. Despite calls for greater investment, substantive innovation in implementation science methodology has not yet materialized. Development of relevant and accessible methodology and software to enable sound evidence-based practice stands to contribute importantly to meeting the Millennium Development Goals; specifically, ending the HIV/AIDS epidemic, providing universal access to AIDS treatment, and reducing maternal and under-5 child mortality. The United States, as the world?s leading funder of HIV/AIDS relief and other global health initiatives, has a vital stake in an evidence-based deployment of this massive investment in improving health and alleviating human suffering worldwide. The purpose of this proposal is to advance the emerging field of implementation science by developing a comprehensive translational science analytics toolkit?a much needed resource for investigators, practitioners, and policymakers who seek to drive today?s global and domestic health agenda through the integration of research findings and evidence into healthcare policy and practice. The principal investigator will undertake a fundamental shift in her career trajectory, to develop new methods and adapt existing methodologies in biostatistics, epidemiology, health economics and computer science to the needs of the translational research agenda. A primary output will be userfriendly software suitable to the requirements and data platforms of translational researchers across the biomedical spectrum. Specific innovations to be pursued are causal inference methods for observational research with missing data; stepped wedge and two stage study designs; and inference for cost effectiveness analysis and for monitoring and evaluation The development and dissemination of the comprehensive toolkit proposed here will pave the way for widespread adoption of evidencebased evaluation and decision-making of programs and interventions in hospitals, by Medicare, and by local, state and national governments around the world.